---
{"title": "JAX vs Numba in 2026: Pricing, Setup and Features", "description": "JAX, Numba and PyOverdrive compared on what each does, what their sources say about price, how each is installed and what to check before you pick.", "url": "https://saydeploy.com/jax-vs-numba/", "date": "2026-10-23"}
---
JAX, Numba and PyOverdrive compared on what each does, what their sources say about price, how each is installed and what to check before you pick.

JAX compiles NumPy programs for accelerators [^1], Numba turns a subset of Python and NumPy code into machine code [^2], and PyOverdrive patches supported NumPy functions with one call [^3]. Each source describes a different job, so match the tool to your own code and hardware.

| Option | Price | License | Install | Python version |
|---|---|---|---|---|
| JAX | Not published | Apache-2.0 [^1] | CPU: pip install -U jax [^1] | Not published |
| Numba | Not published | Open source [^2] | Not published | Python 3.9-3.12 [^2] |
| PyOverdrive | Not published | Open source under the MIT License [^3] | pip straight from the GitHub repository [^3] | Python 3.12 or newer [^3] |

## The short answer

JAX uses a compiler that runs NumPy programs on TPUs, GPUs and other hardware accelerators [^1]. Numba is an open source compiler that turns Python code into machine code as it runs, translating a subset of Python and NumPy code into fast machine code [^2]. PyOverdrive lets a developer call pyoverdrive.enable() so supported NumPy functions use faster versions of the NumPy functions in existing code [^3].

If you need NumPy programs compiled and scaled on TPUs, GPUs and other accelerators, JAX's page says it does that [^1]. Numba's page says it supports NVIDIA CUDA for writing parallel GPU algorithms from Python [^2]. A faster version of the function runs only when a check confirms the input is in its measured range; anything else, or a faster version that raises an error, goes to stock NumPy [^3].

## What JAX and Numba are built for

**JAX.** JAX compiles and scales your NumPy programs so they run on TPUs, GPUs and other hardware accelerators [^1]. It is licensed under Apache-2.0 [^1].

**Numba.** Numba's page calls it an open source compiler that turns Python code into machine code as it runs, translating a subset of Python and NumPy code into fast machine code [^2]. The page says Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN [^2].

It lists support for Intel and AMD x86, POWER8/9, and ARM CPUs including Apple M1 [^2]. It also says it supports NVIDIA CUDA for writing parallel GPU algorithms from Python [^2].

## What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site.

Calling pyoverdrive.enable() patches supported NumPy functions so your existing code uses faster paths, and calls it does not support run on stock NumPy [^3]. A faster version of the function runs only when a check confirms the input is in its measured range, and anything else, or a faster version that raises an error, goes to stock NumPy [^3].

It also has multi-core versions of np.sin, cos, tan, exp, log, log10 and tanh for large arrays, used only when an array is big enough that the multi-core version pays off [^3]. Large calls to np.einsum use a faster method that plans the computation for them [^3]. np.isin on one-dimensional text arrays can use a faster lookup method that stores values for quick matching [^3].

For checking, python -m pyoverdrive --selfcheck compares every faster version of each function with stock NumPy on your own machine [^3]. pyoverdrive.explain() tells you which method a call would use and why, without running the call [^3].

## Pricing and fees

The JAX sources given here state a license, Apache-2.0 [^1]. They do not state a price, so a price for JAX is not published here.

Numba's page calls it an open source compiler that turns Python code into machine code as it runs [^2]. The Numba sources given here state no price and no paid plan, so none is published here.

PyOverdrive is open source under the MIT License [^3]. Its claims state no price and no paid tier, so a price is not published here.

None of the three sources lists a fee on top of another service's fee, so there is no add-on charge to compare. Check each project's own pages if you need more detail on support or hosting costs.

## Setting it up and daily use

**JAX.** The CPU install command is pip install -U jax [^1]. The sources here say nothing more about its setup or routine use.

**Numba.** The page lists Python 3.9-3.12 [^2]. For daily use it says Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN [^2]. The sources here give no install command for Numba, so check its own page.

**PyOverdrive.** You install it with pip straight from the GitHub repository [^3]. It needs Python 3.12 or newer [^3]. The package is marked OS independent, and the test suite has been run on Windows and on Linux x86-64 [^3]. The version is 1.0.0 and its status is Beta [^3].

Calling pyoverdrive.enable() makes supported NumPy functions use the faster versions [^3]. python -m pyoverdrive --demo times the headline operations with stock NumPy and with PyOverdrive on your machine, in about 20 seconds [^3]. python -m pyoverdrive --calibrate checks the multi-core versions on your computer and turns off any that are slower there [^3]. You can turn off one faster version or all of them, and call pyoverdrive.disable() to restore the original NumPy functions [^3].

## Things to check before you choose

Ask whether your code fits the tool's scope. Numba translates a subset of Python and NumPy code [^2], and PyOverdrive patches supported NumPy functions [^3]. Check your own functions against the list of supported NumPy functions in PyOverdrive's own pages.

Check your Python version. Numba's page lists Python 3.9-3.12 [^2], and PyOverdrive needs Python 3.12 or newer [^3]. The JAX sources here give no Python version, so check its page.

Check your hardware. JAX's page names TPUs, GPUs and other accelerators [^1], and Numba's page names several CPU families [^2] and NVIDIA CUDA [^2].

Check project status. The JAX sources here state only its license and install command, so check its own page for project status. PyOverdrive is version 1.0.0 in Beta [^3].

Check whether results match what you have now. PyOverdrive offers a comparison with stock NumPy on your machine [^3]. For JAX and Numba, check their own pages.

## How to choose

If you want NumPy programs compiled for TPUs, GPUs and other accelerators, JAX's page says that is what it does [^1].

If you want to write parallel NVIDIA CUDA algorithms from Python, Numba's page says it supports that [^2].

If you want compiled numerical algorithms that can approach the speeds of C or FORTRAN, Numba's page says that [^2].

If you want existing NumPy code to use faster versions of the NumPy functions after one call, with unsupported calls running on stock NumPy, PyOverdrive's claims describe that [^3].

If you want to see the effect on your own machine first, PyOverdrive's claims describe a demo that takes about 20 seconds [^3].

## Frequently asked questions

### Is JAX or Numba faster?

The sources here give no head-to-head comparison. Numba's page says Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN [^2]. JAX's page says it compiles NumPy programs for TPUs, GPUs and other accelerators [^1]. Speed depends on your code and hardware, so time both on your own workload.

### How do you install JAX and Numba?

The CPU install command for JAX is pip install -U jax [^1]. The Numba sources here give no install command, so check its own page. Numba's page lists Python 3.9-3.12 [^2]. PyOverdrive installs with pip straight from the GitHub repository [^3] and needs Python 3.12 or newer [^3].

### Is JAX or Numba open source?

JAX is licensed under Apache-2.0 [^1]. Numba's page calls it an open source compiler that turns Python code into machine code as it runs, translating a subset of Python and NumPy code into fast machine code [^2]. The Numba sources here state no license detail beyond that, so check its own page if you need the exact terms.

### Is JAX an official Google product?

The sources here do not say who stands behind JAX, so check the project's own pages for that. It is licensed under Apache-2.0 [^1]. Check the project's own pages for support terms and future plans.

### What hardware does Numba support?

Numba's page says it supports Intel and AMD x86, POWER8/9, and ARM CPUs including Apple M1 [^2]. It also says it supports NVIDIA CUDA for writing parallel GPU algorithms from Python [^2]. For anything beyond that list, check Numba's own pages.

### Can PyOverdrive speed up existing NumPy code without rewriting it?

Calling pyoverdrive.enable() patches supported NumPy functions so your existing code uses faster versions of the NumPy functions, and calls it does not support run on stock NumPy [^3]. A faster version of the function runs only when a check confirms the input is in its measured range [^3]. pyoverdrive.disable() restores the original NumPy functions [^3].

It is open source under the MIT License. [^3] - [Visit PyOverdrive](/go/jax-vs-numba)

## Sources

[^1]: JAX, github.com/jax-ml/jax, read 2026-10-07 (https://github.com/jax-ml/jax)
[^2]: Numba, numba.pydata.org, read 2026-10-07 (https://numba.pydata.org)
[^3]: PyOverdrive's own description of itself (the product of this site's publisher) (/go/jax-vs-numba)